Short imagery task (SIT) research method

ABSTRACT

The present disclosure relates to biologically and behaviorally based methods of measuring audience response to a short stimulus.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. application Ser. No.14/230,418, titled “Short Imagery Task (SIT) Research Method,” and filedon Mar. 31, 2014. U.S. application Ser. No. 14/230,418 is a continuationof U.S. application Ser. No. 13/089,752 (now U.S. Pat. No. 8,684,742),titled “Short Imagery Task (SIT) Research Method,” and filed on Apr. 19,2011. U.S. application Ser. No. 14/230,418 also claims priority to U.S.Provisional Application No. 61/325,794, titled “Short Imagery Task (SIT)Research Method,” and filed on Apr. 19, 2010. U.S. application Ser. No.14/230,418, U.S. application Ser. No. 13/089,752, and U.S. ProvisionalApplication No. 61/325,794 are incorporated herein by this reference intheir entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to biologically and behaviorally basedmethods of measuring audience response to a short stimulus.

BACKGROUND

There are many different kinds of audio, visual and audio-visualpresentations and activities that people are exposed to every day. Thesepresentations serve as sensory experiences that stimulate our senses andare known to result in biologically based responses that can be measuredelectronically and mechanically (for example, heart rate, respirationrate, blood pressure, and skin conductance).

A commonly used approach in making measurements for evaluating thesepresentations is that of interrogation, wherein the television/mediaviewer and/or Internet user and/or game player is asked to identifyhimself or herself as a member of the television/media audience or as anInternet user or as a game player. In connection with televisionviewing, this inquiry is usually done by means of an electronicprompting and data input device (for example, as in a Portable PeopleMeter by Arbitron, Inc.) associated with a monitored receiver in astatistically selected population and monitoring site. The memberidentification may also include age, sex, and other demographic data. Itis common to store both the demographic data and the tuning dataassociated with each monitored receiver in the statistically selectedmonitoring site in store-and-forward equipment located within themonitoring site and to subsequently forward these data to a centraloffice computer via a direct call over the public switched telephonenetwork, or via the Internet, on a regular basis. However, thesenon-biologically based self-report methods of measuring audienceresponse are known to be highly error prone.

In fact, personal logs are subjective resulting in recall biases, homemonitoring devices require event-recording by the person and suffer lowcompliance, while digital monitoring of cable and internet signalscannot identify which household member or members are in the audiencenor can they evaluate the level of responsiveness by those members.Other methods of self-report offer valuable data, but are highly errorprone and cannot track the moment-to moment responses to mediaconsumption and participation in interactive activities.

In particular, with the development of the internet and its expansioninto many everyday activities, people are constantly exposed tointeractive media and activities. Nonetheless, the ability to measureand evaluate the user experience, effectiveness, and the usability ofthese interactive media has been limited. In fact, current methodologiesfor measuring or evaluating user experience, effectiveness, andusability of websites and other interactive internet and software mediahas thus far been limited to traditional self-report and eye-tracking onan individual user basis. These prior art techniques involved asking theindividual user questions about the experience and evaluating where theuser was looking during the interactive activity. Some companies (e.g.,NeuroFocus, EmSense) also incorporate EEG in the process and somecompanies propose to measure cognitive activity (e.g., Eye Tracking,Inc.) from pupillary responses. These companies use these measures inattempts to determine emotional states, such as happiness and to studythe effects on implicit memory.

With previous methods known in the art used to analyze responses tostill images, phrases, sounds, words or brief productions (i.e., <15seconds), individuals typically utilize self-report methods oralternatively methods exclusively. These earlier testing methods reliedon examining physiological responses in each individual channel;however, to date, no method exists that combines multiple physiologicalresponse and self-report responses to calculate a single score that ispredictive for a population. Thus, a need in the art exists for a methodthat is capable of integrating self-report and physiological data andcapable of integrating data across multiple physiological channels intoa single score.

SUMMARY

The present disclosure is directed to a method of determining a measureof response of an audience to a target stimulus including:

providing a biometric sensor device operable to measure at least a firstbiometric parameter and a second biometric parameter;

providing each participant an eye tracking device;

exposing each participant of the audience to a presentation over aperiod of time wherein the presentation includes a first series ofstandardized stimuli, at least one target stimulus, and a second seriesof standardized stimuli and wherein each participant is exposed to anull exposure following exposure to each stimulus;

providing a computer system operable to receive data representative ofthe at least two biometric parameters, wherein the computer furtherincludes a memory for storing the data;

re-exposing each participant to the at least one target stimulus;

providing each participant at least one self-report question;

calculating a single physiological score for each participant based onthe data collected on the at least two biometric parameters;

calculating an Emotional impact Score for the audience using eachparticipant's single physiological score; and

calculating an Explicit Emotion Score.

In one embodiment, the first series of standardized stimuli includesbetween 4 and 20 standardized images and the second series ofstandardized stimuli includes between 4 and 20 standardized images. Inanother embodiment, the biometric sensor device is operable to measureat least a third biometric parameter. In yet another embodiment, themethod further includes plotting the Emotional impact Score and theExplicit Emotion Score for the audience on a biphasic graph.

The present disclosure also relates to a method of determining a measureof response of an audience to a target stimulus including:

providing each participant a biometric sensor device capable ofmeasuring at least two biometric parameters;

providing each participant an eye tracking device;

exposing each participant to a series of standardized stimuli, whereineach standardized image is followed by a null exposure;

exposing each participant to a first target stimulus, wherein the targetstimulus is followed by a null exposure;

exposing each participant to a series of standardized stimuli, whereineach standardized image is followed by a null exposure;

measuring at least two biometric parameters during each exposure;

providing a computer system connected to the biometric sensor operableto receive data representative of the at least two biometric parametersand operable to integrate the data across channels into a singlephysiological score; re-exposing each participant to each targetstimulus;

providing each participant at least one self-report question;

calculating a single physiological score for each participant based onthe data collected on the at least two biometric parameters;

calculating an Emotional Impact Score fur the audience using eachparticipant's single physiological score; and

calculating an Explicit Emotion Score.

In one embodiment, the audience includes at least 10 participants. Inanother embodiment, the method further includes a Top of the Mind Task.In yet another embodiment, the method further includes plotting theEmotional Impact Score and the Explicit Emotion Score for the audienceon a biphasic graph.

The present disclosure is also directed to a method of determining ameasure of response of an audience to a target stimulus including:

providing a first biometric sensor device operable to measure at leastone biometric parameter;

providing a second biometric sensor device operable to measure at leasttwo biometric parameters;

providing each participant an eye tracking device operable to determineone or more gaze locations over a presentation where at least oneparticipant is looking;

exposing each participant of the audience to a presentation over aperiod of time wherein the presentation includes a first series ofstandardized stimuli, at least one target stimulus, and a second seriesof standardized stimuli and wherein each participant is exposed to anull exposure for a period of time following exposure to each stimulus;

providing a computer system operable to receive data representative ofthe at least two biometric parameters, wherein the computer furtherincludes a memory for storing the data;

re-exposing each participant to the at least one target stimulus;

providing each participant at least one self-report question;

calculating a single physiological score for each participant based onthe data collected on the at least two biometric parameters;

calculating an Emotional Impact Score for the audience using eachparticipant's single physiological score; and

calculating an Explicit Emotion Score for the audience using eachparticipant's response to the at least one self-report question.

In one embodiment, the method further includes plotting the EmotionalImpact Score and the Explicit Emotion Score for the audience on abiphasic graph. In another embodiment, the participant is provided atleast three self-repost questions. In yet another embodiment, eachparticipant is exposed to the standardized stimuli and the at least onetarget stimulus for between about 5 seconds and about 20 seconds, andwherein each participant is exposed to a null exposure for between about5 seconds and about 15 seconds. In still another embodiment, the methodfurther includes providing each participant with at least threeself-repose questions and calculating an Explicit Emotion Score for theaudience using each participant's response to the at least threeself-report questions.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages of the disclosure can be ascertainedfrom the following detailed description that is provided in connectionwith the drawings described below:

FIG. 1 is an example of a biphasic graph;

FIG. 2 is a schematic diagram of a system according to an embodiment ofthe disclosure for audience measurement in a test theater or facility;

FIG. 3A is a schematic diagram of a second embodiment of the systemaccording to the disclosure for audience measurement in the home;

FIG. 3B is a flow diagram of the in-home compliance algorithm for thesecond embodiment;

FIG. 3C is a flow diagram of one aspect of the in-home systemembodiment, its ability to identify who in a given household is actuallyexperiencing media;

FIG. 4 is a schematic diagram of the third embodiment of the systemaccording to the disclosure for monitoring levels of engagement duringsocial interaction;

FIG. 5 is a schematic diagram of a system according to an embodiment ofthe disclosure for audience measurement of an interactive activity; and

FIG. 6 is a schematic diagram of a system according to an embodiment ofthe disclosure for audience measurement of an alternate interactiveactivity.

DETAILED DESCRIPTION

The present disclosure is directed to a short imagery task (SIT)research method and system for measuring an audience's biometric(physical, behavioral, biological and self-report) responses to asensory stimulus and determining a measure of the audience's engagementto the sensory stimulus. In particular, the disclosure is directed to amethod and system for measuring one or more biometric responses of oneor more persons being exposed to a sensory stimulus, presentation orinteractive activity for brief periods of time. Furthermore, thedisclosure can be used to determine whether the presentation orinteractive activity is more effective in a population relative to otherpresentations and other populations (such as may be defined bydemographic or psychographic criterion) and to help identify elements ofthe presentation that contribute to the high level of engagement and theeffectiveness and success of the presentation.

There are many different kinds of audio, visual, and audio-visualpresentations that people are exposed to every day. These presentationsserve as stimuli to our senses. Many of these presentations are designedto elicit specific types of responses. In some instances, an artist,musician, or movie director has created a presentation that is intendedto elicit one or more emotions or a series of responses from anaudience. In other instances, the presentation is intended to educate orpromote a product, a service, an organization, or a cause. There arealso applications where the audience is exposed to or interacts with oneor more live persons such as during a focus group, during an interviewsituation, or any such social interaction. The audience can also bepresented with an interactive activity or task that can include one ormore audio, visual and audio-visual presentations and allows theaudience to interact with a computer, an object, a situation, anenvironment, or another person to complete an activity or task.Additionally, the participants or audience may be asked to hold orphysically manipulate an object. For example, the participants may beasked to handle a product.

These sensory stimuli can be in the form of a sound or a collection ofsounds, a single picture or collection of pictures or an audio-visualpresentation that is presented passively such as on television or radio,or presented in an interactive environment such as in a video game, liveinteraction or internet experience. The sensory stimuli can bepre-recorded or presented live such as in a theatrical performance orlegal proceeding (passive) or a real-world situation (virtual reality orsimulation) such as participating on a boat cruise, focus group, onlineactivity, board game, computer game, or theme park ride (interactive).

The SIT method of the present disclosure combines a mixture of biometricmeasures (specifically skin conductance, heart rate, respiratory rate,and pupil dilation) with a self-report technique in order to assessstimuli displayed for brief periods of time and ranking along twodimensions. The dimensions are referred to as Emotional Impact andExplicit emotion. The present disclosure is directed to methods forobtaining these scores and utilizing them for market research purposes.Another aspect of this disclosure involves generating graphs for stimuliusing a bi-dimensional graph or biphasic graph, as shown in FIG. 1,based on the information collected according to the SIT method. Usingthe SIT method and biphasic graphs, marketers can make decisions onwhich stimuli to utilize and how to utilize stimuli in marketingexecutions by understanding how people respond to them physiologicallyand consciously.

Responses that are based in human biology can have multiple physiologicand behavioral correlations. One aspect of the disclosure includescollecting at least one measurement by tracking a participant's eyes.The eye-tracking measures can include, but are not limited to, visualattention as estimated by gaze location, fixation duration, and movementwithin a localized area. Another aspect of the present disclosureincludes collecting biometric measurements from the participants.Biometric measures may include, but are not limited to, pupillaryresponses, skin conductivity, heart rate, heart rate variability,brain-wave activity and respiration activity. A third aspect of thepresent disclosure includes collecting behavioral data from theparticipants. Behavioral type biometric responses can include, but arenot limited to, facial micro and macro-expressions, head tilt, headlean, body position, body posture, body movement, and amount of pressureapplied to a computer mouse or similar input or controlling device.Self-report type biometric measures can include, but are not limited to,survey responses to items such as perception of the experience,perception of usability or likeability of experience, level of personalrelevance to user, attitude toward content or advertising embedded inthe content, intent to purchase product, game or service, and changes inresponses from before and after or pre-post testing.

In one aspect of the present disclosure, the data plotted on a biphasicgraph is analyzed according to a prototypical quadrant interpretation.In an embodiment of the disclosure, a prototypical quadrantinterpretation includes labeling the upper right quadrant as the optimalquadrant, the lower right quadrant as the secondary quadrant, the upperleft quadrant as the power quadrant, and the lower left quadrant as theneglect quadrant. The SIT method of the present disclosure can be usedto plot data for a target stimulus in one of these quadrants. Aninvestigator or consultant can then provide descriptive analyses basedon the quantitative data for each target stimulus. For example, if theEmotional Impact Score and the Explicit Emotion Score result in a valuethat is plotted in the upper right quadrant of the biphasic graph shownin FIG. 1, then the stimulus falls within the optimal quadrant.

In an embodiment of the present disclosure, concepts with scores thatfall within the optimal quadrant may be described as having goodstopping power, able to generate unconscious and conscious emotionalresponse and learning, and able to activate approach emotions. Theseconcepts may also be described as effective in a wide variety ofsettings.

Similarly, concepts with scores that fall within the secondary quadrantmay be described as lacking stopping power, able to generate less of anunconscious response, but able to activate approach emotions. Theseconcepts may also be effective when paired with more attention pullingstimuli or placed in a context that draws attention to them.

Concepts with scores that fall within the power quadrant may bedescribed as having good stopping power and able to activate animmediate unconscious response. However, these concepts also canactivate withdrawal emotions. These concepts may be effective when usedto garner attention, activate need states, and when placed in a contextthat involves them in a larger context (i.e., with other concepts ortext) designed to create approach emotions. It may be preferable to usethese concepts sparingly.

Concepts with scores that fall within the neglect quadrant may bedescribed as lacking stopping power, incapable of generating anunconscious response, and rarely able to activate approach emotions.These concepts may also have questionable utility based on theirinability to generate an impact either consciously or unconsciously, andthey may also be easily ignored.

In some embodiments of the present disclosure, it may be preferable tomodify the standard or base quadrant interpretations according to thespecific study questions, the specific study stimuli, and the plan forusing the stimuli.

The present disclosure embodies a research method that allowsinvestigators to assess rapid reactions and conscious reactions tostimuli. The method describes a process for calculating an EmotionalImpact Score and an Explicit Emotion Score to create charts and to plotbiphasic graphs. Investigators can utilize this information by providingit to third parties or to act as consultants. In one aspect of thedisclosure, the information provided by the method is used to achievevarious marketing objectives. In another aspect of the disclosure, theinformation provided by the method is used to evaluate any type ofstimulus.

In an embodiment of the present disclosure, the data collection methodentails at least a three-step process. The first step of the processentails collecting information for use in calculating an EmotionalImpact Score. The second step of the process entails collecting data foruse in calculating an Explicit Emotion Score. Finally, the raw data areused to calculate an Emotional Impact Score and an Explicit EmotionScore, both of which are used to plot a stimulus on a biphasic graph. Itshould be understood that additional data may be collected in additionto the information required for calculating the Emotional Impact Scoreand the Explicit Emotion Score. For example, in a further embodiment ofthe present disclosure, gaze locations are collected for a samplepopulation. As will be discussed in more detail below, gaze locationsmay be used to generate biometric emotive and biometric cognitive maps.

Emotional Impact Score

The Emotional Impact Score is calculated using reference to a database.The Emotional Impact Score is a measure of how a target stimulus fitswithin a database distribution with regard to its standardized distancefrom the database mean using the database standard deviation. Thus, theEmotional Impact Score for a given stimulus compares the reaction of apopulation sample to standardized reactions of database representativeof a larger population.

To collect information for calculating the Emotional Impact Score of thepresent disclosure, individuals in a sample population complete a taskseparately after providing consent to participate in testing. Thegeneral testing procedure includes at least one baseline exposure orexposure to a standardized media immediately followed by a nullexposure, for example a blank screen or silence. After establishing thebaseline parameters for measurement through exposure to standardizedmedia, the participants are then exposed to at least one targetstimulus, which is immediately followed by a null exposure. Once aparticipant views all of the target stimuli in the task, the participantviews a second series of standardized exposures, each being separated bya null exposure.

In another aspect of the disclosure, following consent, each participantis outfitted with a system capable of detecting multiple biometricmeasures, such as the Innerscope® Biometric Monitoring system thattracks heart rate, skin conductance, and respiratory rate. Theparticipant is then placed in front of an eye-tracker capable ofcapturing pupil dilation. Prior to beginning the task, each participantis given a series of instructions by a moderator informing them aboutthe nature of the task and what they are about to see. Once the taskbegins, the participant is first exposed to a series of standardizedimages. In an embodiment of the disclosure, the participant is exposedto at least four standardized images before being exposed to one or moretarget stimuli. In another embodiment of the disclosure, the participantis exposed to at least four standardized images after being exposed toone or more target stimuli.

Standardized images for use with the present disclosure may consist ofthe same media as the target stimuli. For example, if the target stimuliconsist of still images, the standardized images of may be simple stillimages depicting basic imagery (e.g. animals, furniture, or landscapes).The standardized images may be placed on the screen for a pre-selectedamount of time. For instance, the standardized images may be placed onthe screen for approximately 5 seconds. In another embodiment, thestandardized images are placed on the screen for more than 5 seconds.Following exposure to a standardized image, the participant then sees ablank screen or null exposure. The participant may be exposed to a blankscreen for between about 5 seconds and about 15 seconds. Preferably, theparticipant views a blank screen for about 10 seconds. After temporarilyviewing the blank screen, the next standardized image will appear. Thisprocess may repeat for multiple standardized images. In one aspect ofthe disclosure, the process may repeat for between 4 and 20 standardizedimages. Preferably, the process will repeat for between 4 and 8standardized images. In another embodiment of the present disclosure,the process repeats for more than 8 standardized images. In certainaspects of the disclosure, more than 8 standardized images may beemployed in order to include product-related and/or brand relatedimages.

Following exposure to a series of standardized images, the participantis exposed to at least one target stimulus. The terms “target stimulus”and “target stimuli” refer to whatever media are being evaluated forparticipant reaction. In an embodiment of the present disclosure,participants are exposed to target stimuli for at least 5 seconds. In apreferred embodiment of the present disclosure, participants are exposedto target stimuli for up to 15 seconds. In a most preferred embodimentof the present disclosure, participants are exposed to target stimulifor more than 5 seconds, but less than 12 seconds. After exposure toeach of the at least one target stimulus, the participant then sees ablank screen or null exposure, so that there is a period of no exposurein between the target stimuli and after the final target stimulus. Theblank screen is preferably displayed to the participant for at least 10seconds prior to the next target stimulus or standardized image.

It will be understood that the target stimuli of the present disclosuremay represent any sort of media, and the null exposure will be specificto the media being tested. For instance, the participants may be exposedto noises or music, in which case the participants would experience aperiod of silence between exposure periods. Other sorts of media for usewith the present disclosure include images, commercials, sounds, music,phrases, print ads, and the like.

Throughout the task, the biometric monitoring system and eye tracker, inaddition to other optional measuring devices, are connected (by a wireor wirelessly) to a computerized data processor that can receive thedata and apply the described methodologies. As the data is collected,the physiological responses are integrated across channels into a singlephysiological score.

Using the raw data collected during the task, the Emotional impact Scoreis calculated according to the following procedures:

1. The average z-intensity response over the time duration the stimuluswas presented is calculated for each participant.

2. After an average z-intensity is calculated for each participant,those values are averaged across all participants to produce a compositeaverage z-intensity.

3. The composite average z-intensity is then compared to a database ofscores and given a z-score value based on its relationship to thedatabase mean and distribution.

4. The z-score is the Emotional Impact Score that is plotted on thebiphasic graph.

5. The z-score may also be converted to a t-score for additionalanalyses.

Intensity Score

Each measure of intensity can be associated with point in time or awindow or bin of time or event marker within the exposure period. Thisassociation can be accomplished using many methods. Preferably, themethodology for associating a measure of intensity with a point in timeor a window of time within the exposure period is the same or similarfor each measure of engagement determined in a population sample. Forexample, in one method, a given measure of intensity associated with achange in a biologically based response is assigned to the time slot orwindow that corresponds to where one half the rise time of that responseoccurs.

For example, the input to the data processor 16 can be an N by M datamatrix where N is the number of subjects and M is the number of timepoints during which the biological response is recorded. The dataprocessor 16 can include one or more software modules which receive thebiological response data and generate the N by M matrix that is used insubsequent processing steps. The data processor 16 can include anintensity processing module which receives the N by M matrix ofbiological response data, calculates one or more standardized scores foreach biological response measured and each time slot. The output can bea total integer score of the intensity of response across subjects intime windows of W seconds width (this is a variable parameter thatdepends on the presentation). The fractional rise time parameter(f-rise) can be used to estimate the related time window or slot inwhich the response occurs. For example, if a change in a, biologicallybased response occurs over three time slots or windows, W1, W2, W3, andone half the rise-time of the response occurred during window W2, themeasure of intensity for the change in response would be associated withwindow W2. Alternatively, the measure of intensity could be associatedwith the window that contained the peak (i.e., window W3) or the windowthat contained the trough (i.e., window W1). In addition, a fractionalstandard deviation parameter (f-std) can be used to estimate the degreeof the change in response from baseline.

As a result, for each person, a response map can be determined as a setof intensity values associated with each time (or event) window duringwhich each person was exposed to the presentation. The measure ofintensity for the sample population can be determined by adding themeasure of intensity associated with the same time window for eachperson exposed to the presentation. The result is a response time linethat is the aggregate of the population sample. The response patternsfor two or more biologically based responses (e.g., skin conductivity,heart rate, respiration rate, motion, etc.) can be combined (evenly orunevenly weighted) in a time window by time window basis to determine anoverall intensity score or intensity time line. The aggregate can benormalized for a population size, for example 10 or 25 people.

In accordance with the disclosure, the response map or pattern can beused to evaluate radio, print and audio-visual advertisements (for bothtelevision and the Internet), television shows and movies. In oneembodiment, a population sample can be exposed to one or more knownsuccessful advertisements (TV shows, movies, or websites) and then thesame or a different population sample can be exposed to a newadvertisement (TV show, movie, or website). Where the response patternis similar to the response pattern to one or more known successfuladvertisements (TV shows, movies, or websites) it would be expected thatthe new advertisement (TV show, movie, or website) would also besuccessful. Further, a database of response patterns for different typesof stimuli (advertisements, TV shows, movies, websites, etc.) could bemaintained and analyzed to determine the attributes of a successfuladvertisement, TV show, movie, or website.

In accordance with the disclosure, the data processor 16 can include asynchrony processing module which receives the N by M matrix ofbiological response data, calculates the inverse variance of the rate ofchange of one or more biological measures across at least a portion ofthe sample population and determines a standardized value representativeof the synchrony for a given time slot. The data processor 16 candetermine the synchrony of a given biological response by evaluating theslope of the response in a given time window or event window over theperiod of exposure for each person in the population sample. For eachtime window, a slope value can be assigned based on the value of theslope, for example, the greater the slope the greater the slope value.The slope value for each corresponding time window or event window ofeach person of the population sample can be processed to determine ameasure of the variance over the population sample for each time windowor event window. For example, the mean and standard deviation of theslope value of the population sample for each time window or eventwindow can be determined and used to further determine the residualvariance. The residual variance can be further normalized and used toproduce a response pattern that indicates the time-locked synchrony ofthe response of the population sample to the stimulus.

Similarly, the synchrony response map or pattern can be used to evaluateradio, print and audio-visual advertisements (for both television andthe Internet), television shows and movies. Further, the stimulidescribed can be evaluated using both the intensity response pattern andthe synchrony response pattern.

The intensity score can be calculated according to the following steps.

Step 1: Following a noise reduction process for each input channel, foreach participant, the distribution of amplitudes of responses includingthe mean (μ) and standard deviation (σ) of responses is calculated oversome baseline period (this is a variable parameter that depends on thestimulus).

Step 2: For each participant, the location and timing of the trough andpeak amplitude of each response is estimated and the difference betweeneach peak and trough (the amplitude of response) is calculated.

Step 3: The values so determined are used to establish a score for eachindividual response thus: score 0 if the amplitude is less than thebaseline μ for that channel, score 1 for a response if the amplitude isbetween μ and μ+f−(σo), and score 2 for a response if the amplitude isgreater than μ+f−(σ).

Step 4: Each response score for each participant is assigned to asequential bin of variable length time-locked to the media stimulus bylocating the time of the f-rise.

Step 5: The sum of all the binned response scores across allparticipants is calculated for each biological sensor. The score isnormalized depending on the number of sensors collected (being equal foreach test) and the number of participants (being unequal for each test).The score thus created is the intensity score per unit time or per timeslot.

Depending on the sensors used and the presentation being experienced,not all channels will be added to the intensity score. For example,certain forms of respiration (such as a sigh indicative of boredom) ormotion (taking a drink or looking at a watch) may actually be subtractedfrom the intensity score. In addition, alternative versions of theintensity measure may be used for presentations with differing goals.For example, when testing a horror movie, sensors such as skinconductance may be weighted more heavily in the calculation because thegoal of the content is to generate arousal while testing a comedy, whichis meant to elicit laughter, might use stronger weighting towards therespiratory response.

Synchrony Score

Synchrony is a measure of the rate of change of a response by theaudience (plural members of the sample population) to a portion of thestimulus or presentation. The audience can be exposed to the stimulus orpresentation over a period of time or through a sequence of steps orevents. The period of exposure can be divided into windows or portionsor events that correspond to elements or events that make up thestimulus or presentation. For example, the synchrony of the response canbe determined as a function of the rate of change of a biologicallybased response to a portion of the stimulus or an event during thepresentation by a plurality of audience members or the populationsample.

In accordance with the disclosure, the input to the data processor 16can be an N by M data matrix where N is the number of subjects and M isthe number of time points during which the biological response isrecorded. The data processor 16 can include a synchrony processingmodule which receives the N by M matrix of biological response data,calculates an inverse variance across the matrix values and determinesone or more standardized scores for each biological response measuredand each time slot. The output will be a total integer score of thesynchrony of response across subjects in time windows of W seconds width(this is a variable parameter that depends on the stimulus). Inaccordance with the disclosure, the synchrony of a given response isdetermined by evaluating the rate of change of the response in a giventime window or slot over the period of exposure for each participant inthe test audience.

The synchrony score can be calculated according to the following steps.

Step 1: Following a noise reduction process for each input channel,create a sliding window of variable width moving forward in timeincrements that are smaller than the window size.

Step 2: In each window, for each participant, compute the firstderivative of one or more of the response endpoints.

Step 3: Across all participants, calculate the mean (μ) and the standarddeviation (σ) of the rate of change in each window.

Step 4: From the above compute a score=1n|σ−μ|.

Step 5: Scale the resultant score so that all numbers are between 0 and100.

Step 6: Compute the windowed scores commensurate with the intensityscore windows by averaging the sliding scores into sequential windows ofvariable length time-locked to the media stimulus. The score thuscreated is the synchrony score per unit time or per time slot.

Engagement Score

The intensity and synchrony scores may be added together to compute themoment-to moment engagement score per unit time or per time slot.Depending on the nature of the test presentation and the test audience,one of the intensity and synchrony scores may be weighted relative toother. For example, for some tests it may be preferred to identify themost extreme responses and thus intensity would be weighted moreheavily. Alternatively, different functions can be used to determinedifferent forms of the engagement score. For example, multiplyingintensity by synchrony creates exaggerated graphs more readable andusable in some situations such as when evaluating multiple hours oftrial testimony, it may be useful to identify the most extreme examplesof engagement.

Explicit Emotion Score

To collect information to calculate the Explicit Emotion Score,participants engage in at least a two-part task. The first part of thetask involves a Top of the Mind task (TOM), and the second part of thetask involves a self-report survey.

The TOM task involves re-exposing the participants to the targetstimuli, after which the participants are asked to record the firstthing that comes into their mind about each stimulus. The informationcollected from the TOM task is preferably reserved for qualitativeanalysis only. Next, the participants are re-exposed to each targetstimulus and asked to answer multiple self-report questions about thetarget stimulus. In one aspect of the disclosure, participants areprovided the target stimulus as a reference while answering thequestions about that specific target stimulus. Self-report questions foruse with the present disclosure are designed to probe the level oflikeability, the valence of emotional response, and interest for a givenstimulus. In one aspect of the disclosure, responses to questionsprobing the level of likeability, the valence of emotional response, andthe interest for a given stimulus are combined into a single score. Inone embodiment, participants are asked to answer three self-reportquestions. In another embodiment of the disclosure, participants areasked to answer less than three self-report questions. In yet anotherembodiment of the present disclosure, participants are asked to answermore than three self-report questions. In addition to the self-reportquestions, participants may also be asked to answer additional questionsthat are not included in the calculation of the explicit emotion score.

Self-report questions may be constructed generally or may be specific toelements of the target stimulus. In the event a participant is asked torespond to multiple target stimuli, the self-report questions aremaintained stable across the stimuli.

For example, the participants may be asked to answer the threerepresentative questions below with responses based on a scale of 1 to9.

1. Please rate how much von disliked vs. liked this [stimulus] using the9-point scale below.

very much very much disliked disliked neutral liked liked 1 2 3 4 5 6 78 9

2. Please rate how bored vs. interested you feel when looking at this[stimulus] using the 9-point scale below.

very very bored bored neutral interested interested 1 2 3 4 5 6 7 8 9

3. Please rate how bad vs. good you feel when looking at this [stimulus]using the 9-point scale below.

very very bad bad neutral good good 1 2 3 4 5 6 7 8 9

In this aspect of the disclosure, where the word [stimulus] appears inthe questions, a description of the target stimulus is provided. Forexample, if the target stimulus was a picture, the word picture or imagewould be used where the word [stimulus] currently appears.

The Explicit Emotion Score can then be calculated using the valuesprovided in each participant's responses to the three questions. First,the values of the self-report questions are averaged across participantsfor each stimulus. Then the scores are converted to z-scores for thesample. A constant is added to all z-scores. The constant may be between−0.5 and 0.5. In one embodiment of the disclosure, the constant added toall z-scores is 0.5. The Explicit Emotion z-scores are plotted on thebiphasic graph for each stimulus. Additionally, the z-scores areconverted to t-score distributions in order to chart the data.

There are many commercially available products and technologies thatallow continuous unobtrusive monitoring of biometrically andbehaviorally based human responses most often employed for health andfitness purpose. One product, offered under the name LifeShirt System(VivoMetrics, Ventura Calif.) is a garment that is worn unobtrusively bya person being evaluated and can simultaneously collect pulmonary,cardiac, skin, posture and vocal information for later analysis. TheEquivital system (Hidalgo, Cambridge UK), can collect heart rate,respiration, ECG, 3-axis motion and can integrate sun conductance.Similar features are also offered by the Bioharness system (ZephyrTechnologies, Auckland, New Zealand), the Watchdog system (QinetiQ,Waltham, Mass.), BT2 Vital Signs wristwatch (Exmocare, Inc., New York,N.Y.) and Bionode systems (Quasar, San Diego Calif.). Another product,offered under the name Tobii x50 Eye Tracker or Tobii 2150 (TobiiTechnology, McLean, Va.) is an eye-tracking device that allows forunobtrusive monitoring of eye-tracking and fixation length to a highdegree of certainty. By combining eye-tracking with a biologically basedengagement metric, the system can uniquely predict which specificelements within a complex sensory experience (e.g., multimediapresentation or website) are triggering the response. This technologyalso records additional biometric measures, such as pupillary dilation.Other companies developing this technology include Seeing Machines,Canberra, Australia.

Another technology, developed at the MIT Media Lab, (MIT, Cambridge,Mass.) provides a system for measuring behavioral responses including,but are not limited to, facial micro and macro-expressions, head tilt,head lean, and body position, body posture and body movement. Anothertechnology, developed at the MIT Media Lab, (MIT, Cambridge, Mass.)provides a system for measuring behavioral responses including, but notlimited to, the amount of pressure applied to a computer mouse orsimilar controlling device. In some aspects of the present disclosure,the eye tracking device may be in the form of goggles or headgear thatcan be worn while a participant physically holds or manipulates a targetstimulus.

While many systems have been put forward for identifying individualemotions, no system has been proposed that can reliably and objectivelyquantify specific and overall responses to passive and interactiveaudio, video, and audio-video content. One likely reason for thisfailure is the complexity and subjectivity of human emotionalexperience. Rather than use individual biological responses to identifyindividual emotions in individual participants, the present disclosureis designed to aggregate biologically based responses of a population tocreate a moment-to-moment or event based impact of the stimulus orpresentation. This can be accomplished according to one embodiment ofthe disclosure by determining measures of intensity of responses acrossthe sample population.

As set forth briefly above, the present disclosure is directed to amethod and system for collecting data representative of variousbiometrically based responses of a person (or animal) to a passive orinteractive presentation. The presentation can include an audio, visualor audio-visual stimulus, such as a sound or sequence of sounds, apicture or a sequence of pictures including video, or a combination ofone or more sounds and one or more pictures, including video. Thestimulus can be pre-recorded and played back on a presentation device orsystem (e.g., on a television, video display, projected on a screen,such as a movie) or experienced as a live performance. The stimulus canbe passive, where the audience experiences the stimulus from astationary location (e.g., seated in a theater or in front of atelevision or video screen) or the stimulus can be interactive where theaudience is participating in some form with stimulus (e.g., live rollercoaster ride, simulated roller coaster ride, shopping experience,computer game, virtual reality experience or an interactive session viathe internet). The data collected can be processed in accordance withthe disclosure in order to determine a measure of Emotional impact andthe Explicit Emotion of the sample population (or animal).

The measure of Emotional Impact and the Explicit Emotion for a samplepopulation can further be used to predict the level of engagement andimpact of a larger population. In the context of this disclosure, thesample population audience can include as many participants as theinvestigator requires. Furthermore, the period of exposure can bedivided into time slots or windows, or event based units and a responsevalue determined for and associated with each time slot or event window.

The system can include three time-locked or synchronized sources ofdata: 1) a media device for presenting a sensory stimulus or series ofstimuli, 2) a monitoring device for the collection of a plurality ofbiological responses to the sensory stimulus, and 3) an eye-trackingsystem and/or video camera to determine the location and duration ofpupil fixation, dilation and facial responses. Additional video camerascan be used to determine the proximity of the individual and/or audienceto the media device and the specific elements of the sensory stimulusbeing experienced. The biometric response monitoring device and theeye-tracking system and/or video camera can be synchronized with themedia device presenting the sensory stimulus so that the monitoringdevice and the eye-tracking system and/or video camera can consistentlyrecord the biometric responses and gaze location, duration and movement,that correspond to same portions of the presentation for repeatedexposures to the presentation. The system sensor package can include,but is not limited to, a measure of skin conductivity, heart rate,respirations, body movement, pupillary response, mouse pressure,eye-tracking and/or other biologically based signals such as bodytemperature, near body temperature, facial and body thermographyimaging, facial EMG, EEG, FMRI and the like.

The test media content can include, but is not limited to, passive andinteractive television, radio, movies, internet, gaming, and printentertainment and educational materials as well as live theatrical,experiential, and amusement presentations. The three time-locked datasources can be connected (by wire or wireless) to a computerized dataprocessor so the response data can be transferred to the computerizeddata processor. The computerized data processor can automatically applythe described methodologies of scoring, resulting in a map of engagementper unit time, per event, or aggregated across the entire test samplepopulation or stimuli.

The system is further able to use eye-tracking, directional audio and/orvideo, or other technology to isolate specific elements or moments ofinterest for further in-depth processing, in accordance with thedisclosure, the system can track what content is being viewed, who isviewing the content and which physical, behavioral, and biologicalresponses of the audience members correspond to the viewed content on amoment-to-moment basis or on a per event basis.

The system can provide an objective view of how an audience will respondto a passive or interactive presentation. The system can further includea database of biometrically based audience responses, response patternsand audience intensity, synchrony and engagement patterns and levels,and performance metrics (as may be derived therefrom) to a variety ofhistoric media stimuli that, when combined with demographic and otherdata relevant to the test media content, allows for a prediction of therelative success of that content, presentation or interactiveexperience.

For the purposes of this disclosure, the sample audience is preferablyat least 20 participants who are monitored viewing the same content oneor more times. Monitoring of audiences can be done individually, insmall groups, or in large groups, simultaneously or as different times.The audience can be of a tightly defined demographic/psychographicprofile or from a broadly defined demographic/psychographic profile or acombination of the two. The system records the time-locked or eventlocked data streams, calculates the level of moment-to-moment or eventbased Emotional Impact, and compares the values to a database of similarmedia content.

The system can use eye-tracking or other technology to isolate specificelements, areas or moments of interest for further analysis orprocessing. In accordance with the disclosure, the system can track whatcontent is being viewed, who is viewing the content (including by genderand demographic/psychographic profile), which areas or sub-areas of thecontent are being focused on by each individual and which measuredresponses of the audience correspond to the viewed content. Thus, for agiven piece of stimulus content in a passive or interactivepresentation, the measured responses can be connected with the portionof the content that elicited the response and the data from more thanone sample audience or a subset of sample audiences gathered atdifferent times and places can be aggregated.

In accordance with another embodiment, participating members of ahousehold can control their media choice and usage throughout the courseof their day while they wear a sensor device (for example, a specialarticle of clothing, a bracelet or other device) that measures somecombination of responses as they watch television, listen to music, oruse the internet. In this embodiment, the in-home sensing devicecommunicates with an in-home computer or set top box (STB) thatdetermines the nature and timing of the media content the participanthas chosen as well as identifying information about the participant. Thesystem would include a technology that could determine the distance fromthe media stimulus such as distance measurement via technologies likeinfrared, global positioning satellite, radar or through the acquisitionof a signal between two objects, such as the television or computer andparticipant using technologies with a known range of operation (e.g.,WiFi, Zigbee, RFID, or Bluetooth) and/or the direction of theparticipant eye-gaze (e.g., using eye-tracking technology).

In a variant of this embodiment, the STB or computer can preventactivation of home media devices unless the sensor device was activatedto ensure compliance. In another variant of this embodiment, testpresentation content and/or broadcast/cable presentation content can be“pushed” to the participant that “matches” a desireddemographic/psychographic profile or predetermined level or pattern ofengagement. As in prior embodiments, the system can record thelime-locked or event based data streams, calculate the moment-to-momentor event based level of engagement relative to that person, and comparethe pattern of engagement to a database of similar individualexperiences.

In accordance with another embodiment, the presentation that providesthat sensory stimulus can be a live person or persons or activity. Thislive person or persons may include, but is not limited to, live focusgroup interactions, live presentations to a jury during a pre-trial ormock-trial, an interview-interviewee interaction, a teacher to a studentor group of students, a patient-doctor interaction, a dating interactionor some other social interaction. The live activity can be an activity,for example, riding on a rollercoaster, in a boat or in a car. The liveactivity can be an everyday activity like shopping in a store,performing yard work or home repair, shopping online or searching theinternet. The live activity can also be a simulated or virtual realitybased activity that simulates any known or fictional activity.

The present disclosure relates to a system and method for use in thefield of audience measurement. A system is described for recording thebiometrically based audience responses to a live or recorded, passive orinteractive audio, visual or audio-visual presentation that provides asensory stimulating experience to members of the audience.

The system can further integrate time-locked or event lockedeye-tracking and other video monitoring technology with the measure ofengagement to identify specific elements of the sensory stimulus thatare triggering the responses. The system can also use the measure ofengagement to anticipate the relative success or failure of the teststimulus via predictive models using a database of historic patterns ofengagement for similar test stimuli in similar audiences.

FIG. 2 shows a schematic diagram of an embodiment of the systemaccording to the disclosure. The presentation is presented to theaudience 12 via a display device 10, such as a video display screen orother commercially available technology for presenting the presentationto the test or sample audience 12. The presentation can include, but isnot limited to, passive and interactive television, radio, movies,internet, gaming, and print entertainment and educational materials. Thedisplay device 10 can include but is not limited to a television, moviescreen, a desk-top, hand-held or wearable computer device, gamingconsole, home or portable music device or any other device for thepresentation of passive or interactive audio, visual or audio-visualpresentation. For the purposes of this disclosure, the test audience 12can be any small or large group defined by any number of parameters(e.g., demographics, level of interest, physiological or psychologicalprofile) who is monitored viewing the content one or more times. Thetest audience can be monitored using a monitoring system 12A for thecollection of a plurality of physical, behavioral, and biologicalresponses and a self-report device 12B for the collection of self-reportresponses, all time-locked or event locked to each other and the teststimulus or interactive presentation. The system can include a focusand/or facial monitoring system 14 (e.g., eye-tracking system, or one ormore digital video cameras C) for the collection of data on thebehavior, facial response and/or precise focus of the individual membersof the audience. These data-sources (media stimulus, measured responsedata, and focus data) can be synchronized or time-locked and/orevent-locked to each other whereby the response data collected isassociated with a portion of the presentation and sent to a computerdata processing device 16. The computer data processing device can be ageneral purpose computer or personal computer with a processor, memoryand software for processing the biological response data and generatingthe intensity, synchrony and engagement values. The data sources can betime-locked, event-locked or synchronized externally or in the dataprocessor 16 by a variety of means including but not limited to startingthem all at the same time, or by providing a common event marker thatallows the each system (in data processor 16) collecting the data fromthe three data sources to synchronize their clocks/event timers orsimply synchronizing the clocks in each of the systems or use a commonclock. The data processing device 16 can run software that includes thescoring algorithm to calculate the moment-to-moment, event-to-event ortotal level of Emotional Impact and compares it to a database of otheraudience responses to the same or similar test presentations, orstandardized presentations, and delivers the results to a user-interface18. The user interface 18 can be provided on a desktop or portablecomputer or a computer terminal that accesses data processor 16. Theuser interface 16 can be a web based user interface or provided by adedicated client miming on the desktop or portable computer or computerterminal. The results can be interpreted and collected into a printed orelectronic report 20 for distribution. The response data can beassociated with the portion of the presentation that was displayed whenthe response was measured. Alternatively, the response data can beassociated with an earlier portion of the presentation that is presumedto have caused the response based on a determined delay.

The monitoring device 12A for measuring biometric responses can includeany of a number of commercially available or other sensors known in theart for measuring such responses. In accordance with the disclosure, theleast invasive and obtrusive sensors with the most comfortable formfactor should be chosen to minimize disruption of the experience.Preferably, the sensors should allow participants to experience thepresentation or test stimulus “as if” they were not being monitored atall. Form factors include but are not limited to wearable devices suchas “smart” garments, watches, and head-gear and remote sensing devicessuch as microphones, still and video cameras. Many devices are availableand known to collect measures of the autonomic nervous system, facialmusculature, motion and position, vocal features, eye-movements,respiratory states, and brain waves. Multiple combinations of sensorscan be used depending on the sensory stimulus, population, and locationof the monitoring.

The self-report device 1213 can be any of the well known devices forpermitting an audience member to report their response to a presentationor interactive activity. Typically, self-report devices 1213 include aknob, a slider or a keypad that is operated by the audience member toindicate their level of interest in the presentation. By turning theknob, moving slider or pressing a specific button on the keypad, theaudience member can indicate their level of interest in the presentationor interactive activity. Alternatively, self-report device 12B can be acomputer keyboard and/or mouse that an audience member can use tointeract with the presentation. Mouse movements in association withicons or elements on the computer screen can be used to indicate levelsof interest. In addition, the mouse or other input device can includesensors, such as force and pressure sensors for measuring the forcesapplied to the mouse by the audience members. Alternatively, keyboardkeys (up arrow, down arrow, page up and page down), can used to indicatelevels of interest. In addition, the user can type in responses toquestions or select answers to multiple choice questions.

Predictive Modeling

The system can further include a database of audience engagement to avariety of historic media or other relevant stimuli or experiences thatwhen combined with demographic/psychographic profiles and other datarelevant to the test content that allows for a prediction of therelative success of that content in a similar population. After testingan audience, various forms of the output from the described method canbe used to estimate the likelihood of the success of the sensorystimulus in achieving its goal. The statistical analyses for creatingpredictive models can include, but are not limited to, variables relatedto the product or the content itself, the price of sale or cost ofproduction of the product or content, the place of purchase or medium ofexperience, the cost of promotion, and/or the characteristics of theaudience. For example, factors included in a model for the televisionindustry may include but are not limited to: a) number of viewers pertime slot, b) ratings of the lead-in show, c) ratings of the followingshow, d) mean ratings for the type of show, e) lead actor/actresspopularity rating, f) time of year, g) advertising revenue, h)promotional budget for the show, and/or i) popularity of the network.Other factors may include but are not limited to characteristics of thetarget audience such as: a) reported liking of the show, b)psychographic characteristics (e.g., introversion vs. extroversion), c)demographic characteristics, and/or d) ability to recall or recognizeelements of the show. Indicators of success can include but are notlimited to how likely a population with similar characteristics is towatch the television show outside of a testing theater and/or how likelya population with similar characteristics will remember and/or purchasethe products being advertised. Preferably, the more people tested (thelarger the sample population) and the better characterized thepopulation, the more likely that the model can be an accurate predictorof a larger population response. The preferred predictor model caninclude, but is not limited to, any of the following statisticalmethods: a) mixed media models, b) traditional multivariate analyses, e)hierarchical linear modeling, d) machine learning, e) regressionanalyses, f) Bayesian shrinkage estimators, and/or g) cluster and factoranalyses.

FIG. 3A shows a schematic diagram 200 of a second embodiment of thesystem according to the disclosure. In this embodiment, the mediastimulus is presented via commercially available video signals 22, suchas the cable TV signal and plugs into the STB 22A. In turn, the STB 22Aenables programs to be displayed on the media device 24 such as a TVmonitor, computer, stereo, etc. In this system, a participant 30 inviewing distance wearing a wireless sensor package in an unobtrusiveform factor like a bracelet 32 interacts with the media device. Inaddition, bracelet 32, one or more video cameras (or other known sensingdevices, not shown) can provided to measure, for example, eye trackingand facial expressions and other physical and behavioral responses. Aslong as that person is in basic viewing distance, the sensor receiver26, which can be a separate unit or built into the STB 22, will receiveinformation about that participant. The system 200 can time-stamp orevent stamp the measured responses along with the unique identifier ofthat participant. This data can be time-stamped or events stamped withrespect to the programming currently being played by the participant.This information can be sent back to a central database 216 via atransmission network 28 such as an internet connection, pager, orcellular network.

FIG. 3B shows a flow diagram 210 of the in-home compliance algorithm toimprove usage of the in-home embodiment of this disclosure. In ahousehold where this system can be set up, compliance can be dealt withby controlling the ability to change programming on the media devicebeing used. The STB 22A can be programmed such that it will not function(partially or completely) if the sensor device is not being worn and isnot active. If the sensors are being worn or charging, the STB can beprogrammed to work. If, however, the sensors are not being worn and arefully charged, the STB can be programmed not to respond fully orpartially. In a partial functionality mode, only certain stations may beavailable, for example, public access and emergency stations. The flowchart 210 of the operation involves a receiver 26 that checks 44 to seeif it is getting a signal 42 from the sensor or sensors, which is onlypossible if the sensor is activated and is being worn. If the receiveris getting a signal, it waits a set amount of time before starting over46. If it does not receive a signal, the system checks whether a sensordevice is being charged in the attached cradle 48, if so and the batteryis not full, it also waits a set interval before checking again 50. If,however, the sensor is not active, not charging or fully charged and notbeing used, the STB can become inactive until the next check shows achange 52.

FIG. 3C shows one aspect of the in-home system, i.e., its ability toidentify who in a given household is actually watching. The wirelesstechnology involved in connecting the sensor with the receiver sends outa unique identifier. This identifier will be related to the data sentout in order to identify the source of the biometric data and link it tothe current media stimulus. Anyone wearing a sensor but not in thedefined wireless range from the receiver will not have their informationtracked while outside of that range. The system will wait for a periodtime 68 if no wireless signal is received. If they are in the range ofanother receiver 62 (and STB 26) and the signal is received 62, however,their information can be tracked by that system. The flow chart 220involves a wireless technology 26 (e.g., Bluetooth) that is used toconnect the sensor device to the receiver or STB 22A. Wirelesscommunications can be used to establish a connection 66 and transferdata between the receiver (not shown) and the STB 22A as well as totransfer data needed to determine compliance above. Once a participantis identified, information regarding that participant is collected andsent 70 to the database (DB) and processed as above 74 to generatereports for distribution.

FIG. 4 shows a schematic diagram of the third embodiment of the system300 according to the disclosure. In this embodiment, the sensorystimulus can be a live person 310 and the system and method of thedisclosure can be applied to a social interaction that can include, butis not limited to, live focus group interactions, live presentations toa jury during a pre-trial or mock-trial, an interview-intervieweeinteraction, a teacher to a student or group of students, apatient-doctor interaction, a dating interaction or some other socialinteraction. The social interaction can be recorded, such as by one ormore audio, still picture or video recording devices 314. The socialinteraction can be monitored for each individual 312 participant'sbiologically based responses time-looked to each other using abiological monitoring system 312A. In addition, a separate or the samevideo camera or other monitoring device 314 can be focused on theaudience to monitor facial responses and/or eye-tracking, fixation,duration and location. Alternatively, one or more head mounted cameras314 (for example, helmet mounted or eyeglass mounted) can be used toprovide eye tracking data. The data-sources can be time-locked or eventlocked to each other and sent to a computer data processing device 316.The data processing device 316 can run software that includes thescoring algorithm to calculate the moment-to-moment or event basedpatterns of engagement and compares it to a database of other audienceresponses to the same or similar media test stimulus and deliver theresults to a user-interface 318. The results can be processed in apredictor model as described above and interpreted and collected into areport 320 for distribution.

In accordance with an alternative embodiment of the disclosure, anaudience (one or more individuals) is exposed to one or more an audio,visual or audio visual stimuli (such as a presentation or items ofcontent) that are interactive and can be separated into events. An eventis the exposure or interaction with a stimulus at a specific time andfor a specified duration. Typically, the stimuli or presentation can bepresented on a computer screen or a large format television screen andcan be used in connection with a system that accepts user (audiencemember) input, using, for example, a mouse, a keyboard or a remotecontrol.

In accordance with an embodiment of the disclosure, the system canmeasure one or more responses and event-lock or time-lock the measuredresponse(s) to the portion of the stimuli (for example, the portion ofthe interactive presentation) being presented to or experienced by theindividual audience member at the time of the response. In addition,with respect to eye tracking, the system can record the areas ofinterest and visual attention of each member of the audience (for whicheye tracking is provided and enabled). Areas of Interest can includepredetermined target areas, sub-areas, items, creative elements orseries of areas or elements within an interactive presentation (or otherstimulus) used for individual or aggregated analyses of the interactiveactivity. Visual Attention can be measured by non-invasive eye-trackingof gaze fixations, locations, and movement for individuals and it can beaggregated for defined user groups and audience population samples.

In accordance with an embodiment of the disclosure, the system canrecord biometric measures of each member of the audience for one or moreevents during the interactive presentation. Biometric measures caninclude, but are not limited to, pupillary responses, skin conductivityand galvanic skin response, heart rate, heart rate variability,respiratory response, and brain-wave activity. Behavioral type measurescan include, but are not limited to, micro and macro facial expressions,head tilt, head lean, body position, body posture, and the amount ofpressure applied to a computer mouse or similar input or controllingdevice. Self-Report type measures can include, but are not limited to,survey responses to items such as perception of the experience,perception of ease-of-use/usability or likeability of experience, levelof personal relevance to user, attitude toward content or advertisingembedded in the content, intent to purchase product/game or service, andchanges in responses from pre-post testing. Self-report measures canalso include report of demographic information or the use ofpsychographic profiling.

FIG. 5 shows a schematic diagram of a system 500 for exposing a memberof an audience 510 to an interactive presentation provided on a computersystem 520 in accordance with one embodiment of the disclosure. The user510 can interact with the presentation provided on the computer screen522 using a keyboard and/or mouse 524. Sound can be provided by aheadset 526 or speakers (not shown). Additional input devices 526 can beused to receive self-report data, such as, like and dislike informationin the form of a position of a dial or slider on a hand held device 526that includes for example a potentiometer.

The user can be monitored using one or more video cameras 532, one ormore biometric monitoring devices 534 such as biometric sensing shirt534A or bracelet 534B. In addition, mouse 522 can include a pressuresensor or other sensor to detect the pressure applied to the mousebuttons. These sensors 532, 534A, 534B can be used for measuringbiometric responses such as eye tracking, behavioral and biologicresponses. In addition, the computer 520 can be used for measuringand/or recording self-report responses, such as computer generatedsurveys, free text input via the keyboard 522 or audio responses viaheadset 526. The data processing system 540 can present the interactivepresentation to the user 510 according to a predefined program orsequence and record the eye tracking data as well as other biometricresponse data in a manner that links the response data to presentation.The data processing system 540 can be connected to the computer system520 by a wired or wireless network 542 to deliver presentation contentto the computer system 520. The wired or wireless network 542 can alsobe used to deliver sensor response data to data processing system 540for storage and further processing.

Some or all of the sensor data (such as from sensors 532, 534A and 534B)and input data (such as from input devices 522, 524 and 526) can betransferred either by wire or wirelessly to the computer system 520 andfurther transferred to data processing system 540. Alternatively, someor all of the sensor and input data can be transferred directly to thedata processing system 540 by wired or wireless network 542. Network 542can utilize most communication technologies, including RS-232, Ethernet,WiFi, Blue Tooth and Zigbee, for example. In addition, more than onecommunication technology can be used at the same time, for example,network 542 can included wired components (such as, Ethernet and digitalcable) and wireless components (such as, WiFi, WiWAX and Blue Tooth) toconnect different sensors and computer system components to the dataprocessing system 540.

Furthermore, the data processing system 540 can be one computer systemor a cluster or group of computer systems. The response data can belinked or synchronized with the presentation (by aligning usingassociated timestamps or event windows), whereby the response data isassociated with incremental time slots of the presentation.Alternatively, the presentation can be divided into event windows, forexample, based on the specific tasks or activities that are included inthe interactive presentation and the response data can be associatedwith event windows associated with specific tasks or portions of a task.Each task or activity can have one or more event windows associated withit and each event window can have the same or a different duration oftime.

In accordance with one embodiment of the disclosure, the eye tracking,behavioral and other biometric measures (either individually or incombination) can be presented to the user to create conscious awarenessof these responses and improve the accuracy and utility of theself-report measures. The self report measures can be used in additionto the intensity, synchrony and engagement metrics to evaluate theaudience responses to the presentation or activity. The user can beexposed to the interactive presentation and then the user can be exposedto the interactive presentation (or specific portions of thepresentation) a second time and provided with information orrepresentative information of their eye tracking, behavioral and otherbiometric responses and then the user is presented with survey questions(or questionnaires), exposed to one-on-one debriefings or interviews, orinvolved in qualitative focus groups. Alternatively, inquiries can bemade to the user as they view the presentation a second time along withtheir responses to the presentation.

For each presentation, task, process or experience, one or more Flow,Appeal and Engagement indices can also be determined to aid in theassessment and predictability of the overall audience response. Each ofthe measures or indices can be determined or computed using a computersystem according the disclosure using one or more methods according tothe disclosure. The preferred embodiment, one or more of the measures orindices can be determined by a computer software module running on acomputer system according to the disclosure. The computer softwaremodule can be a stand alone program or component of a larger program andcan include the ability to interact with other programs and/or modulesor components.

In accordance with one embodiment of the disclosure, computer system caninclude a computer software module that records, by storing in memory ofthe computer system, the biometric and other data produced by thebiometric sensors and video cameras. The stored biometric and other datacan be associated with a point in time within the time duration of thepresentation or an event window of an activity that serves as thestimulus. This can be accomplished by storing one or more data valuespaired with or linked to a time value or using a database thatassociates one or more stored data values with one or more points intime. After the presentation has ended or the activity is completed,software running on the computer system can process the stored biometricand other data to determine the various measures and indices.Alternatively, the stored data can be transferred to another computersystem for processing to determine the various measures and indices.

The Biometric Cognitive Power index for an event window (or a time slotor time window) can be determined as a function of the portion of theevent time (duration or frequency) during an interactive task, processor experience where the cognitive response (value, amplitude or rate ofchange of value or amplitude) such as, the pupillary response, is abovea predefined threshold (for example, above or below the mean or averageresponse by k*standard deviation, where k can be, for example, 0.5, 1.0,1.5). In other embodiments, other measures of cognitive response can beused as an alternative to or in addition to pupillary response, such asEEG or brain wave activity.

Biometric Cognitive Power index (e) for an event e, can be determined asthe sum of the number of time instants ti (or the portion or percentageof time) in the first T seconds of each subject's experience (which isreferred to as the subject's analysis-duration T) where the cognitiveresponse measured is above the predefined threshold and averaged acrossall subjects viewing the same experience/stimulus. In particular,Biometric Cognitive Power(e)=Average[across all subjects s](sum of(cognitive_response(s,ti))where ti<T and cognitive response (pupil_response)>specified threshold

In one embodiment of the disclosure, the analysis-duration T can be setto the first 5 seconds of the subjects' experience of the event. Inother embodiments, it can be, for example, set between 5-10 seconds. Inother embodiments, it can be set to one-half or one-third of the eventduration or time window.

In one embodiment of the disclosure, a time instant ti can be thesampling rate of the system for the biometric sensor, for example, 20msec. In other embodiments, other units of time can be used, such as0.10 sec. and 0.01 sec.

Where, in this example, the cognitive response measured is a pupillaryresponse function. The function, pupil_response (s, ti) can be theresponse of subject a during event window eat time instant ti, if theresponse differs from the average response for subject s on event e bymore than k*standard deviation, where k can be an analysis-specificthreshold or parameter, for example, between 0.5 and 1.5. The length ofthe analysis-duration can be specific to each stimulus image, event orscene of the presentation.

In accordance with one embodiment of the disclosure, theanalysis-duration T can be determined as one half to one-third the timeneeded for an average individual to process the information shown in theimage, event or scene of the presentation. For instance, if thepresentation consists primarily of a textual document or print materialthen analysis-duration T can be, for example, set in the range of 15-45seconds and begin at the start of the time window or event window orwithin, for example, the first 15 seconds of the time or event window.If the image, event or scene consists primarily of visualobjects/drawings as in a print ad (with very little text information),then the analysis-duration T can be set in the range of 5 to 10 seconds.In an alternative embodiment of the disclosure, the analysis-durationcan be set to the first 5 seconds of an event window or time window. Inother embodiments, the analysis-duration T, can be any unit of time lessthan or equal to the event window or time window and can begin at anypoint during the event window or the time window. For interactiveactivities, for example shopping, the event window can be a unit of timeduring which the audience member selects an item for purchase, makes apurchase or returns an item and the analysis duration T can beginapproximately at the point in time when the audience member selects anitem for purchase, make a purchase or returns an item.

In accordance with one embodiment of the disclosure, the BiometricCognitive Power index determination can be implemented in a computerprogram or computer program module that accesses biometric data storedin memory of a computer system, receives the data from another programmodule or receives it directly from biometric sensors. The data can bereal time data or data that was previously captured from one or moreaudience members and stored for later processing.

In accordance with one embodiment of the disclosure, the parameters,including k and the analysis-duration T can be computed using predictivemodels described in any of the data mining books described herein, byutilizing outcome variables such as a subjects' (or audience member's)behavior (e.g., purchase/return of a product described in the stimulusor event). The data mining books include: Larose, Daniel T., Data MiningMethods and Models, John Wiley & Sons, Inc., 2006; Han, Micheline KamberJiawei, Data Mining: Concepts and Techniques, Second Edition (The MorganKaufmann Series in Data Management Systems), Elsevier, Inc., 2006; Liu,Bing, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data(Data-Centric Systems and Applications), Springer-Verlag, 2007; andBerry, Michael J. A. and Linoff, Gordon S., Data Mining Techniques: ForMarketing, Sales, and Customer Relationship Management, John Wiley &Sons, Inc., 1997; all of which are herein incorporated by reference intheir entirety.

For visual stimuli, such as images, we can, for example, represent the2-dimensional screen area as composed of a grid of size m-by-n cells orpixels. The m and n values will depend on the parameters of the visualstimulus and the computer or TV screen on which the visual stimulus ispresented and can be the pixel resolution of the presentation screen ordetermined as a function of the pixel resolution of the presentationscreen. Typically, m-by-n will be 1280-by-1024 or 640-by-480. In onembodiment of the disclosure, the visual screen can be a 1280-by-1024grid of pixels and the stimulus grid can be represented by a matrix ofgrid cells, for example as 640-by-512 (by defining a grid cell as a 2×2matrix of pixels).

Gaze location can be defined as a set of grid-cells that are determinedto be the focus of an audience member's gaze and represent the set ofgrid cells (0−(m*n)) that an audience member looked at during a time orevent window. If the audience member focused on one grid cell, the gazelocation would be one the grid cell, whereas, if the audience memberfocused on more than one grid cell, the gaze location would be a set ofgrid cells or a function of the set of grid cells (such as the grid cellor set of contiguous grid cells that were the focus for the longesttime). Where a grid cell is defined as more than one pixel, audiencemember focus on any of the pixels in the grid cell is considered gaze onthe location of the grid cell. A gaze location can be used to identify acontiguous area using a set of grid cells on the screen. Alternatively,a gaze location can also represent a group of such contiguous areas,each area being disjoint from one another.

A Biometric Cognitive Map can be produced by plotting the areas ofindividual or aggregated group gaze fixation as a function of abiometric cognitive power index (where the duration or frequency ofcognitive response are above a threshold level) and the gaze locationson the presentation (or image, event or scene therein) corresponding tothe cognitive power index when the stimulus has a visual component, suchas an image or a video. A biometric cognitive map can be used toidentify the areas of a presentation that are associated with higherlevels of responses indicative of high levels of cognitive activity.Specifically, a biometric cognitive map represents the gaze locations oraggregated regions of the locations on the visual portion of thestimulus when the cognitive response for a subject differs from its meanby k*standard deviation, for example, where k can be between 0.5 and 1.5during the analysis-duration for the subject's experience. The gazelocations can be aggregated either across temporal instants for eachsubject (e.g., a subject ‘s’ looking at a location at instants “h” and“h+5”) within the analysis-duration, or across different subjectslooking at the locations within the analysis-duration of theirexperience. A variety of clustering algorithms, such as those describedin data mining hooks disclosed herein, can be employed to createaggregated regions or clusters from a set of specific gaze locations.

In accordance with one embodiment of the disclosure, the BiometricCognitive map can be generated by a computer program, computer programmodule or a set of computer program modules that access biometriccognitive power index data and gaze fixation data that was stored inmemory of a computer system, received from another program module orreceived directly from biometric sensors and the eye tracking system.The data can be real time data or data that was previously captured andstored from one or more audience members.

In accordance with one embodiment of the disclosure, a biometriccognitive plot area can be determined by first plotting gaze locationsin a cognitive map, such as for a specific time or event window, thencreating clusters or aggregated regions and determining the area orrelative area of dusters.

In accordance with one embodiment of the disclosure, the system, inaccordance with the method of the disclosure, can plot the gazelocations that correspond to significant cognitive responses (responsesthat meet or exceed a threshold) in a biometric cognitive map for astimulus (or an event) for all subjects exposed to the stimulus for aperiod more than the analysis-duration. This can, for example, beimplemented in a computer program, a computer program module or set ofcomputer program modules. The gaze locations can be plotted only whenthe cognitive response for a subject is, for example, above or belowdiffers from) the subject's mean response by k*std_deviation, where, forexample, k can be between 0.5 and 1.5. If the response is above themean, the location can be termed a location of high cognitive responseand the locations can be considered high cognitive locations. If theresponse is below the mean response, the location can be termed alocation of low cognitive response and the locations can be consideredlow cognitive locations.

In addition, adjacent high locations and/or adjacent low locations canbe combined based on their proximity (distance to each other) using wellknown clustering algorithms. Examples of clustering algorithms aredisclosed in the data mining books disclosed herein.

In accordance with one embodiment of the disclosure, the clustering canbe accomplished as follows:

1) For each grid cell identifying a high or low location, expand the setof grid cells to include all its neighboring grid cells, 5 grid cells inall directions (i.e., expanding by a circle of radius of 5 centered atthe grid cell) in the cluster. Alternate radii of 10-15 grid cells mayalso be employed.

2) The cluster for a set of grid cells of a kind (high or low) can thusinclude any ‘unfilled gaps’ (unselected grid cells in the area) andidentify one or more contiguous ‘geometric regions’ in the cognitivemap.

3) The low cognitive clusters in a cognitive map will cluster the lowcognitive locations and the high cognitive clusters in a cognitive mapwill cluster the high cognitive locations.

4) The clustering algorithm can be applied iteratively starting with asingle grid cell (or pixel) or set of contiguous grid cells (or pixels)and repeated until a predetermined number of clusters are defined.

The biometric cognitive plot area can have low and high cognitiveclusters identified on or defined for a cognitive map. The system,according to the method of the disclosure, can determine the biometriccognitive plot area by determining the total area of the high and/or thelow cognitive clusters. The biometric cognitive plot area can bemeasured in terms of the number of pixels or grid cells in a cluster orgroup of clusters, or as a proportion (or percentage) of the total areaof the presentation screen or a portion of the presentation screen (suchas, a quadrant or a region).

In accordance with one embodiment of the disclosure, the BiometricCognitive plot area can be determined using a computer program, computerprogram module or a set of computer program modules that accessbiometric data and gaze fixation data, and/or intermediate dataconstructs (such as, the Biometric Cognitive Power index), that werestored in memory of a computer system, received from another programmodule or received directly from biometric sensors and the eye trackingsystem. The data can be real time data or data that was previouslycaptured and stored from one or more audience members.

The Biometric Emotive Power index for an event window (or a time slot ortime window) can be determined as a function of the portion of the eventtime (duration or frequency) during an interactive task, process orexperience where the emotive response (value, amplitude or rate ofchange of value or amplitude) such as one or more of skin conductance,heart rate, and respiratory responses, is above a predefined threshold(e.g., above or below the mean or average response by k*standarddeviation, where k can be, e.g., 0.5, 1.0, 1.5). In other embodiments,other measures of emotive response can be used as an alternative to orin addition to skin conductance, heart rate and respiratory responses,such as brain wave activity.

Biometric Emotive Power index (e) for an event e, can be determined asthe sum of the number of time instants ti (or the portion or percentageof time in the first T seconds of each subject's experience (which isreferred to as the subject's analysis-duration T) where the emotiveresponse measured is above the predefined threshold and averaged acrossall subjects viewing the same experience/stimulus. For example,Biometric Emotive Power(e)=Average[across all subjects s](sum of(emotive_response(s,ti))where ti<T and emotive response (skin_conductance_response)>specifiedthreshold.

In one embodiment of the disclosure, the analysis-duration T can be setto the first 5 seconds of the subjects' experience of the event. Inother embodiments, it can be, for example, set between 5-10 seconds. Inother embodiments, it can be set to one-half or one-third of the eventduration or time window.

In one embodiment of the disclosure, a time instant ti can be thesampling rate of the system for the biometric sensor, for example, 20msec. In other embodiments, other units of time can be used, such as0.10 sec. and 0.01 sec.

Where, in this example, the emotive response measured is a skinconductance response function. The function, skin_conductance_response(s, ti) can be the response of subject s during event window e at timeinstant ti, if the response differs from the average response forsubject a on event e by more than k*standard deviation, where k can bean analysis-specific threshold or parameter, for example, between 0.5and 1.5. The length of the analysis-duration can be specific to eachstimulus image, event or scene of the presentation.

In accordance with one embodiment of the disclosure, theanalysis-duration T can be determined as one half to one-third the timeneeded for an average individual to process the information shown in theimage, event or scene of the presentation. For instance, if thepresentation consists primarily of a textual document or print materialthen analysis-duration T can be, for example, set in the range of 15-45seconds and begin at the start of the time window or event window orwithin, for example, the first 15 seconds of the time or event window.If the image, event or scene consists primarily of visualobjects/drawings as in a print ad (with very little text information),then the analysis-duration T can be set in the range of 5 to 10 seconds.

In an alternative embodiment of the disclosure, the analysis-durationcan be set to the first 5 seconds of an event window or time window. Inother embodiments, the analysis-duration T, can be any unit of time lessthan or equal to the event window or time window and can begin at anypoint during the event window or the time window. For interactiveactivities, for example shopping, the event window can be a unit of timeduring which the audience member selects an item for purchase, makes apurchase or returns an item and the analysis duration T can beginapproximately at the point in time when the audience member selects anitem for purchase, make a purchase or returns an item.

In accordance with one embodiment of the disclosure, the BiometricEmotive Power index determination can be implemented in a computerprogram or computer program module that accesses biometric data storedin memory of a computer system, receives the data from another programmodule or receives it directly from biometric sensors. The data can bereal time data or data that was previously captured from one or moreaudience members and stored for later processing.

In accordance with one embodiment of the invent ion, the parameters,including k and the analysis-duration T can be computed using predictivemodels described in any of the data mining books described herein, byutilizing outcome variables such as a subjects' (or audience member's)behavior (e.g., purchase ret of a product described in the stimulus orevent).

For visual stimuli, such as images, we can, for example, represent the2-dimensional screen area as composed of a grid of size m-by-n cells orpixels. The m and n values will depend on the parameters of the visualstimulus and the computer or TV screen on which the visual stimulus ispresented and can be the pixel resolution of the presentation screen ordetermined as a function of the pixel resolution of the presentationscreen. Typically, m-by-n will be 1280-by-1024 or 640-by-480. In onembodiment of the disclosure, the visual screen can be a 1280-by-1024grid of pixels and the stimulus grid can be represented by a matrix ofgrid cells, for example as 640-by-512 (by defining a grid cell as a 2×2matrix of pixels).

Gaze location can be defined as a set of grid-cells that are determinedto be the focus of an audience member's gaze and represent the set ofgrid cells (0−(m*n)) that an audience member looked at during a time orevent window. If the audience member focused on one grid cell, the gazelocation would be one the grid cell, whereas, if the audience memberfocused on more than one grid cell, the gaze location would be a set ofgrid cells or a function of the set of grid cells (such as the grid cellor set of contiguous grid cells that were the focus for the longesttime). Where a grid cell is defined as more than one pixel, audiencemember focus on any of the pixels in the grid cell is considered gaze onthe location of the grid cell. A gaze location can be used to identify acontiguous area using a set of grid cells on the screen. Alternatively,a gaze location can also represent a group of such contiguous areas,each area being disjoint from one another.

A Biometric Emotive Map can be produced by plotting the areas ofindividual or aggregated group gaze fixation as a function of abiometric emotive power index (where the duration or frequency ofemotive response are above a threshold level) and the gaze locations onthe presentation (or image, event or scene therein) corresponding to theemotive power index when the stimulus has a visual component, such as animage or a video. A biometric emotive map can be used to identify theareas of a presentation that are associated with higher levels ofresponses indicative of high levels of emotive activity. Specifically, abiometric emotive map represents the gaze locations or aggregatedregions of the locations on the visual portion of the stimulus when theemotive response for a subject differs from its mean by k*standarddeviation, for example, where k can be between 0.5 and 1.5 during theanalysis-duration for the subject's experience. The gaze locations canbe aggregated either across temporal instants for each subject (e.g., asubject ‘s’ looking at a location at instants “h” and “h+5”) within theanalysis-duration, or across different subjects looking at the locationswithin the analysis-duration of their experience. A variety ofclustering algorithms, such as those described in data mining booksdisclosed herein, can be employed to create aggregated regions orclusters from a set of specific gaze locations.

In accordance with one embodiment of the disclosure, the BiometricEmotive map can be generated by a computer program, computer programmodule or a set of computer program modules that access biometricemotive power index data and gaze fixation data that was stored inmemory of a computer system, received from another program module orreceived directly from biometric sensors and the eye tracking system.The data can be real time data or data that was previously captured andstored from one or more audience members.

In accordance with one embodiment of the disclosure, a biometric emotiveplot area can be determined by first plotting gaze locations in aemotive map, such as for a specific time or event window, then creatingclusters or aggregated regions and determining the area or relative areaof clusters.

In accordance with one embodiment of the disclosure, the system, inaccordance with the method of the disclosure, can plot the gazelocations that correspond to significant emotive responses (responsesthat meet or exceed a threshold) in a biometric emotive map for astimulus (or an event) for all subjects exposed to the stimulus for aperiod more than the analysis-duration. This can, for example, beimplemented in a computer program, a computer program module or set ofcomputer program modules. The gaze locations can be plotted only whenthe emotive response for a subject is, for example, above or below(i.e., differs from) the subject's mean response by k*std_deviation,where, for example, k can be between 0.5 and 1.5. If the response isabove the mean, the location can be termed a location of high emotiveresponse and the locations can be considered high emotive locations. Ifthe response is below the mean response, the location can be termed alocation of low emotive response and the locations can be considered lowemotive locations.

In addition, adjacent high locations and/or adjacent low locations canbe combined based on their proximity (distance to each other) using wellknown clustering algorithms. Examples of clustering algorithms aredisclosed in the data mining books disclosed herein.

In accordance with one embodiment of the disclosure, the clustering canbe accomplished as follows:

1) For each grid cell identifying a high or low location, expand the setof grid cells to include all its neighboring grid cells, 5 grid cells inall directions (i.e., expanding by a circle of radius of 5 centered atthe grid cell) in the cluster. Alternator radii of 10-15 grid cells mayalso be employed.

2) The cluster for a set of grid cells of a kind (high or low) can thusinclude any ‘unfilled gaps’ (unselected grid cells in the area) andidentify one or more contiguous ‘geometric regions’ in the emotive map.

3) The low emotive clusters in an emotive map will cluster the lowemotive locations and the high emotive clusters in an emotive map willcluster the high emotive locations.

4) The clustering algorithm can be applied iteratively starting with asingle grid cell (or pixel) or set of contiguous grid cells (or pixels)and repeated until a predetermined number of clusters are defined.

The biometric emotive plot area can have low and high emotive clustersidentified on or defined for an emotive map. The system, according tothe method of the disclosure, can determine the biometric emotive plotarea by determining the total area of the high and/or the low emotiveclusters. The biometric emotive plot area can be measured in terms ofthe number of pixels or grid cells in a cluster or group of clusters, oras a proportion (or percentage) of the total area of the presentationscreen or a portion of the presentation screen (such as, a quadrant or aregion).

In accordance with one embodiment of the disclosure, the BiometricEmotive plot area can be determined using a computer program, computerprogram module or a set of computer program modules that accessbiometric data and gaze fixation data, and/or intermediate dataconstructs (such as, the Biometric Emotive Power index), that werestored in memory of a computer system, received from another programmodule or received directly from biometric sensors and the eye trackingsystem. The data can be real time data or data that was previouslycaptured and stored from one or more audience members.

The eye tracking system can monitor the gaze fixation of each user, on amoment by moment basis or an event basis. The gaze fixation data can beused to identify elements, areas or regions of interest, including areasthat the user or a group of users (that make up the sample audience)spent more time looking at than other areas of a presentation orcorrespond to or are associated with higher cognitive or emotiveresponses than other areas. The system can analyze the eye tracking andthe response data and determine or calculate the plot area of theregion, area or element within the presentation that corresponds to aresponse or combination of responses. The plot area can define theperipheral boundary of an area or region that is of interest.

Using the eye tracking response data and the biometric response data,one or more biometric cognitive maps and biometric emotive maps can begenerated and the biometric cognitive and emotive plot area for eachcognitive and emotive map can also be determined. In accordance with oneembodiment of the disclosure, the Cognitive and Emotive Visual Coverageindices for a category of stimuli (for example, products) can bedetermined as function of the biometric cognitive and emotive plotareas. In one embodiment, the Visual Coverage index can be determined asfunction of the areas of the presentation that are associated witheither high or low (cognitive or emotive) response and the total area ofthe presentation screen or the presentation on the screen.High Cognitive Visual Coverage Index=High Cognitive plot area/Total Area

Where the High Cognitive plot area is the sum of the area of all thehigh cognitive clusters for the stimulus and the Total Area is the totalarea of the presentation gaze area (where the presentation occupies lessthan the whole screen) or the screen.High Emotive Visual Coverage Index=High Emotive plot area/Total Area

Where the High Emotive plot area is the sum of the area of all the highemotive clusters for the stimulus and the Total Area is the total areaof the presentation gaze area (where the presentation occupies less thanthe whole screen) or the screen.Low Cognitive Visual Coverage Index=Low Cognitive plot area/Total Area

Where the Low Cognitive plot area is the sum of the area of all the lowcognitive clusters for the stimulus and the Total Area is the total areaof the presentation gaze area (where the presentation occupies less thanthe whole screen) or the screen.Low Emotive Visual Coverage Index=Low Emotive plot area/Total Area

Here the Low Emotive plot area is the sum of the area of all the lowcognitive clusters for the stimulus and the Total Area is the total areaof the presentation gaze area (where the presentation occupies less thanthe whole screen) or the screen.

Where at least one biometric cognitive map and at least one biometricemotive map are generated, cognitive coverage indices (high and low) andemotive visual coverage indices (high and low) can be determined foreach task, process, experience or event.

In accordance with one embodiment of the disclosure, a Visual impactindex (or area) can be determined as function of the cognitive andemotive coverage indices. The High Visual Impact index (or area) for astimulus or category of stimuli (or products) can be determined as theaverage or the sum of the emotional and cognitive coverage indices.

For example, in accordance with one embodiment of the disclosure theHigh Visual impact index (or area) for a stimulus or category of stimuli(or products) can be, for example, determined as:(High Emotional Visual Coverage Index+High Cognitive Visual CoverageIndex)The Low Visual Impact index (or area) for a stimulus or category ofstimuli (or products) can be, for example, determined as:(Low Emotional Visual Coverage Index+Low Cognitive Visual CoverageIndex)

In accordance with an embodiment of the disclosure, each of the computedbiometric measures described herein, such as, intensity, synchrony,engagement, emotional power index, cognitive power index, emotionalcoverage index, biometric coverage index and visual impact for astimulus can be used to predict or estimate the success rate of thestimulus on a stand-alone or on a comparative basis to other stimuli.The success can be measured by the external response measures of thegeneral or target audience outside the test facility to the content,product or brand represented in the stimuli. The external responsemeasures can include but is not limited to the number of viewerswatching, downloading and/or storing, or skipping/forwarding thestimulus (overall viewing characteristics), the number of comments oramount of buzz that the stimulus or the content referred to in thestimulus generates in offline or online (internet) forums, socialnetworks, communities and/or markets, the number of views of thestimulus (by audience members) in offline or online (internet) forums,social networks, communities and markets, the average rating for thestimulus by the audience, the overall adoption rate (the volume ofproduct sales) by target audience etc.

In accordance with one embodiment of the disclosure 600, as shown inFIG. 6, a sample population of shoppers 610 (individuals seeking topurchase a specific product or product type) can be studied by exposingthem to an active or passive presentation which includes a set ofproducts 620 or products of a specific type. For example, differenttypes and/or brands of Soups 620A, Sauces 620B, Juices 620C, and Salsas620D can be presented, such as on a store shelf. Each shopper 610 can bemonitored while actually shopping in a store for (or being presentedwith a simulated environment or diagram of a store or supermarket shelfshowing) different products, for example, juices, salsas, sauces orsoups), all by the same or a different company (same brand or differentcompanies and brands) and asked to select one or more for purchase, forexample, by taking the product off the shelf or selecting with a mouseor dragging an icon to a shopping cart.

Where the shopper is actually shopping in a store, the shopper can befitted with a camera that is directed to show what the shopper islooking at, for example a helmet mounted camera 632A, or a cameramounted on eye glasses worn by the shopper (not shown). Thus, the camera632A can show what the shopper 610 is looking at during any given timeslot or event window. In addition, the shopper can be monitored usingone or more biometric monitoring devices 634 worn by the shopper duringthe experience, such as biometric sensing shirt 634A or bracelet 634B.Additional cameras 632B can be provided (either mounted or hand held) inthe area of the store that the shopper is viewing to provide pupillaryresponse data.

The response data can be stored in the monitoring devices 634 (or one ormore memory devices associated with one or more of the monitoringdevices) worn by the user, or transferred by wire (not shown) orwirelessly over network 642 to data processing system 640, shown as aportable computer, although a desktop computer or group of computers,can be used as well. Depending on the type of network used, the dataprocessing system can located in any location that can be connected tothe network 642, such as within the store, across the city or across thecountry. The network 642 can be made up of several communicationchannels using one technology or a combination of technologies(Ethernet, WiFi, WiMAX, Blue Tooth, ZigBee, etc.).

Where the data is stored in the monitoring devices (or one or morememory devices associated with one or more of the monitoring devices) anetwork 642 can be used to transfer the data to the data processingsystem 640 after the task or presentation or a set of tasks orpresentation is paused or completed. Alternatively, the stored data canbe transferred to the data processing system 640 by direct wireconnection (not shown) as well. As described here, the data processingcomputer can process the sensor and camera data to generate the variousindices described herein.

Alternatively, the shopper can be fitted only with a helmet mountedcamera 632A or eye glass mounted camera (not shown) and sent on ashopping spree. The shopper can be presented with a video of theshopping experience on a computer, television or video screen whilebeing monitored using a system according to an embodiment of thedisclosure, such as shown in FIG. 5. Thus, an eye tracking system 532and a combination of biometric and behavioral sensing devices 534A, 534Band input devices 534, 526, 528 can be used to monitor response dataassociated with the activity and transfer the response data to the dataprocessing system 540 for further processing. Alternatively, the shoppercan go shopping in a simulated or virtual reality environment.

In each of these presentations, as the shopper 610 views each individualproduct 620A, 620B, 620C, 620D on the shelf, the eye tracking system candetermine which product is being focused on and the biometric responsesof the user can be recorded at that time. The response data, when it isstored, can be associated with a time mark, frame number, or anarbitrary index mark or number of the presentation. In one embodiment,the system records the responses on 20 ms intervals, but longer orshorter intervals can be used depending on the various constraints andrequirements of the system, for example, the speed and size of the datastorage system and the response characteristics of the sensor systemsbeing used and the desired resolution. In accordance with one embodimentof the disclosure, the presentation can provide running time or a frameby frame index or time that allows the system to associate the responsedata with a specific point in time, typically offset from the beginningof the presentation or allows the response data to be associated with aspecific frame number or time index associated with a specific frame.

In other embodiments of the disclosure, the presentation can be markedor associated with predefined event windows that start at a predefinedtime or frame of the presentation and extend for a predefined durationof time. The time between event windows does not have to be constant andthe duration of an event window can be the same or different from oneevent window to the next. In one embodiment, an event window begins whena user is presented with a screen display which involves the user in aninteractive presentation, task or activity and extends for a duration offive (or in some cases, up to ten) seconds. During the five (or ten)second window, the eye tracking, behavior and biometric response datacan be collected on 20 ms intervals, providing up to 250 (or 500 for 10second duration) data points from each sensor for the event window. Somesensors may not provide data at the same frequency and the system candetermine a single elemental value for each response measured on anevent window by event window basis. The single elemental value for theevent window can, for example, be determined as function of the mean,median or mode of the response data received during the time periodcorresponding to the event window.

In accordance with one embodiment of the disclosure, the above metricscan be used to analyze the engagement and visual impact of variousinteractive and passive presentations for various audiences. It has beenfound that the high visual impact index correlates well with thebiometric non-visual intensity (using non-visual, biometric responses,e.g., heart rate, skin conductivity, respiration) at the time ofpurchase or product selection whereas the low visual impact indexcorrelates well with the biometric non-visual intensity at the time ofreturning products back on product shelf.

The Flow index of a task, process or experience can be determined as afunction of measures of task (process, or experience) completionindices, efficiency indices and frustration indices and can includeself-report and biometric responses to further weight or adjust thecompletion index, efficiency index and frustration index. In accordancewith one embodiment of the disclosure, the Flow Index can be determinedby the equation:Flow Index=(Completion Index+Efficiency Index)−Frustration Index

The Completion index can be determined as a function of the percentageof a test group of individual users that completed a task, process orexperience and one or more metrics relating to the time to completion,such as the mean time to completion and the standard deviation over thetest group. Tasks or processes that have a high percentage of completioncan be given a high completion index, and where two or more tasks have asimilar percentage of completion, the tasks with shortest time tocompletion or the smallest deviation in time to completion can beweighted higher than the others.

If compl-time(T) represents the mean time for completion of task T, thencompletion index for task T can be defined as a z-score, such as(compl-time(T)−average of(compl-time(Ti)))/Standard_deviation(compl_time(Ti).

Other functions for the Completion index of task T can also be derived,using predictive models described in the data mining hooks describedherein, by relating the completion times to outcome variables such astest groups behavior (e.g., like/dislike of a task T). Specifictechniques that could be utilized include regression analysis forfinding a relationship between completion times and outcome variablesand using completion index as an indicator of the outcome variable.

The Efficiency index can be determined as a function of gaze fixationand duration over a series of one or more target areas of interest (suchas along a task path). The Efficiency index can be weighted by aself-report measure of ease-of-use and user experience. Tasks orprocesses that have a higher percentage of gaze fixation and duration onthe predefined target areas can be given a higher efficiency index andthis value can be weighted based on the self report responses toquestions and inquiries relating to ease of use and user experience.Efficiency Index for task T with target area set A=Emotive EfficiencyIndex for T with target area set A+Cognitive efficiency Index for T withtarget area set AWhere Cognitive efficiency index for task T with target set A=Highcognitive efficiency index for T with target set A if >0

Otherwise, Low cognitive efficiency index for T with A

High cognitive efficiency index for T with A=sum of areas (geometricintersection of (high cognitive map, A)/Sum of plot areas in highcognitive map.

Low cognitive efficiency index for T with A=(−1)*sum of areas (geometricintersection of (high cognitive map, A)/Sum of plot areas in highcognitive map

Emotive efficiency index for task T with target set A=High emotiveefficiency index for T with target set A if >0

Otherwise, Low emotive efficiency index for T with A

High emotive efficiency index for T with A=sum of areas (geometricintersection of (high emotive map, A)/Sum of plot areas in high emotivemap

Low emotive efficiency index for T with A=(−1)*sum of areas (geometricintersection of (high emotive map, A)/Sum of plot areas in high emotivemap

Other functions for combining the high/low emotive, cognitive efficiencyindexes can also be derived using predictive models, described in thedata mining books described herein, by relating the efficiency indexesto outcome variables such as the test group's behavior (e.g.,like/dislike of a task T). Specific techniques that could be utilizedinclude regression analysis for finding a relationship betweencompletion times and outcome variables and using efficiency index as anindicator of the outcome variable.

The Frustration index can be determined as a function of behavioralresponses that tend to indicate frustration, such as facial expressionsand body movements and system input devices that can measure pressure,such as a pressure sensing computer mouse or other input device (forexample, pressure and repetition of key presses applied to the keys of akeyboard). The frustration index can be weighted by one or more of aself-report measure of frustration and one or more biometric emotivemeasures,Frustration index for task T=Sum of frustration indexes from pressuremouse responses, body movement, key presses, and facial expressions; andFrustration index for task T from pressure mouse=z-score of pressuremouse signals for task T in comparison to a database of tasks T-DB.Likewise, Frustration index for task T from key presses-z-score of keypresses for task T in comparison to a database of tasks T-DB.

The frustration index can also be restricted to specific target areasmentioned in self-report studies. For instance frustration index fortask T from key presses in target area set A can only account for thekey presses within the target area set A.

Other functions for frustration index for Task T can also be derivedusing predictive models, described in the data mining books describedherein, by relating the input variables (key presses, pressure mousesignal values, etc.) to outcome variables such as test group's behavior(e.g., like/dislike of a task T). Specific techniques that could beutilized include regression analysis for finding a relationship betweeninput and outcome variables and assuming frustration index as anindicator of the outcome variable.

The Appeal index of a task, process or experience can be determined as afunction of a weighted combination (of one or more) of self reportresponses for likeability, biometric emotive responses, and behavioralmeasures of micro and macro facial expressions, body or head lean towardthe activity. The Appeal index can provide an indication ofattractiveness by the user to the task, process or experience, with ahigh appeal index indicating a more enjoyable experience.Appeal index for T=sum of (weight(s)*self report(T),weight(b1)*biometricresponses(T,b1),weight(bn)*biometrie responses(T,bn)), for i=1 to n.Where bi is the ith biometric measure of n biometric measures.

Other functions for appeal index for Task T can also be derived usingpredictive models, described in the data mining books described herein,by relating the input variables (self report, head lean values, etc.) tooutcome variables such as test group's behavior (e.g., like/dislike of atask T). Specific techniques that could be utilized include regressionanalysis for finding a relationship between input and outcome variables.

The Engagement index of a task, process or experience can be determinedas a function of the Flow index, Appeal index, Biometric Emotive Powerindex and Biometric Cognitive Power index, for example:Engagement Index=Flow Index+Appeal Index+Biometric Emotive PowerIndex+Biometric Cognitive Power Index

In addition, Biometric Persona or groupings can be created byidentifying a group of users having a similarity of their pattern oftask, process or experience metrics without regard to demographic orpsychographic profile. Note that this grouping can utilize machine-basedclustering algorithms for this grouping, or alternately may involve amanual process of an administrator/expert identifying the groupings orclusters of users.

Other embodiments are within the scope and spirit of the disclosure. Forexample, due to the nature of the scoring algorithm, functions describedabove can be implemented and/or automated using software, hardware,firmware, hardwiring, or combinations of any of these. Featuresimplementing the functions can also be physically located at variouspositions, including being distributed such that the functions orportions of functions are implemented at different physical locations.

Further, while the description above refers to the disclosure, thedescription may include more than one disclosure.

What is claimed is:
 1. A system comprising: a first sensor to measurefirst biometric data from a first subject exposed to a first stimulusvia a stimulus presentation device; a second sensor to measure secondbiometric data from a second subject exposed to the first stimulus, thefirst subject and the second subject forming an audience; a memoryincluding machine executable instructions; and at least one processor toexecute the instructions to: control the stimulus presentation device topresent the first stimulus based on activation of the first sensor;synchronize the first biometric data and the second biometric data, thefirst biometric data and the second biometric data time-stamped withrespective time indicators indicative of a window of exposure of thefirst subject and the second subject to the first stimulus; generate asynchrony score for the first stimulus based on the synchronization;determine a first response score for the first subject based on thetime-stamped first biometric data; determine a second response score forthe second subject based on the time-stamped second biometric data;reconcile differences in frequencies between the first sensor and thesecond sensor by determining a combined response score for the windowbased on the first response score and the second response score;calculate an engagement score for the first stimulus based on thesynchrony score and the combined response score; estimate aneffectiveness of the first stimulus based on the engagement score; andcause at least one of (1) at least a portion of the first stimulus or(2) a second stimulus to be presented based on the estimate.
 2. Thesystem of claim 1, wherein the at least one processor includes a firstprocessor and a second processor, the first processor to transmit theengagement score to the second processor.
 3. The system of claim 1,wherein the first response score is a first intensity response measurefor the first subject to the first stimulus and the second responsescore is a second response intensity measure for the second subject tothe first stimulus and wherein the at least one processor is to:determine the combined response score based at least one of a mean, amedian, or a mode of the first response intensity measure and the secondresponse intensity measure; and calculate the engagement score by one of(1) aggregating the combined response score and the synchrony score, or(2) multiplying the combined response score by the synchrony score. 4.The system of claim 3, wherein the at least one processor is to assign afirst weight to the combined response score and a second weight to thesynchrony score.
 5. The system of claim 1, wherein the at least oneprocessor is to: event-stamp the first biometric data and the secondbiometric data with respective event indicators indicative of exposureof the first subject and the second subject to an event in the firststimulus; and synchronize the first biometric data and the secondbiometric data based on the event indicators.
 6. The system of claim 1,wherein the engagement score includes a first engagement score for theaudience at a first time and a second engagement score for the audienceat a second time and the at least one processor is to: identity a firstengagement pattern based on the first engagement score and the secondengagement score; perform a comparison of the first engagement patternto a second engagement pattern, the second engagement pattern associatedwith a third stimulus; and estimate the effectiveness of the firststimulus based on the comparison.
 7. The system of claim 1, wherein thefirst biometric data includes an action of a subject with a controldevice while exposed to the first stimulus.
 8. A method comprising:controlling, by executing an instruction with a processor, a stimuluspresentation device to present a first stimulus based on activation of afirst sensor; measuring, via the first sensor and a second sensor,respectively, first biometric data from a first subject exposed to thefirst stimulus and second biometric data from a second subject exposedto the first stimulus, the first subject and the second subject formingan audience; synchronizing, by executing an instruction with theprocessor, the first biometric data and the second biometric data, thefirst biometric data and the second biometric data time-stamped withrespective time indicators indicative of a window of exposure of thefirst subject and the second subject to the first stimulus; generating,by executing an instruction with the processor, a synchrony score forthe first stimulus based on the synchronization; determining, byexecuting an instruction with the processor, a first response score forthe first subject based on the time-stamped first biometric data;determining, by executing an instruction with the processor, a secondresponse score for the second subject based on the time-stamped secondbiometric data; reconciling, by executing an instruction with theprocessor, differences in frequencies between the respective sensors bydetermining a combined response score for the window based on the firstresponse score and the second response score; calculating, by executingan instruction with the processor, an engagement score for the firststimulus based on the synchrony score and the combined response score;estimating, by executing an instruction with the processor, aneffectiveness of the first stimulus based on the engagement score; andcausing, by executing an instruction with the processor, at least one of(1) at least a portion of the first stimulus or (2) a second stimulus tobe presented based on the estimate.
 9. The method of claim 8, whereinthe processor includes a first processor and a second processor, andfurther including transmitting the engagement score to the secondprocessor via the first processor.
 10. The method of claim 8, whereinthe first response score is a first intensity response measure for thefirst subject to the first stimulus and the second response score is asecond response intensity measure for the second subject to the firststimulus and further including: determining the combined response scorebased at least one of a mean, a median, or a mode of the first responseintensity measure and the second response intensity measure; andcalculating the engagement score by one of (1) aggregating the combinedresponse score and the synchrony score, or (2) multiplying the combinedresponse score by the synchrony score.
 11. The method of claim 10,further including assigning a first weight to the combined responsescore and a second weight to the synchrony score.
 12. The method ofclaim 8, further including: event-stamping the first biometric data andthe second biometric data with respective event indicators indicative ofexposure of the first subject and the second subject to an event in thefirst stimulus; and synchronizing the first biometric data and thesecond biometric data based on the event indicators.
 13. The method ofclaim 8, wherein the engagement score includes a first engagement scorefor the audience at a first time and a second engagement score for theaudience at a second time and further including: identifying a firstengagement pattern based on the first engagement score and the secondengagement score; performing a comparison of the first engagementpattern to a second engagement pattern, the second engagement patternassociated with a third stimulus; and estimating the effectiveness ofthe first stimulus based on the comparison.
 14. The method of claim 8,wherein the first biometric data includes an action of a subject with acontrol device while exposed to the first stimulus.
 15. A tangiblemachine readable storage device or storage disc comprising instructionswhich, when executed by a first processor, cause the first processor toat least: control a stimulus presentation device to present a firststimulus based on activation of a first sensor; measure, via the firstsensor and a second sensor, respectively, first biometric data from afirst subject exposed to a first stimulus and second biometric data froma second subject exposed to the first stimulus, the first subject andthe second subject forming an audience; synchronize the first biometricdata and the second biometric data, the first biometric data and thesecond biometric data time-stamped with respective time indicatorsindicative of a window of exposure of the first subject and the secondsubject to the first stimulus; generate a synchrony score for the firststimulus based on the synchronization; determine a first response scorefor the first subject based on the time-stamped first biometric data;determine a second response score for the second subject based on thetime-stamped second biometric data; reconcile differences in frequenciesbetween the respective sensors by determining a combined response scorefor the window based on the first response score and the second responsescore; calculate an engagement score for the first stimulus based on thesynchrony score and the combined response score; estimate aneffectiveness of the first stimulus based on the engagement score; andcause at least one of (1) at least a portion of the first stimulus or(2) a second stimulus to be presented based on the estimate.
 16. Thestorage device or storage disc of claim 15, wherein the first responsescore is a first intensity response measure for the first subject to thefirst stimulus and the second response score is a second responseintensity measure for the first subject to the second stimulus andwherein the instructions cause the first processor to: determine thecombined response score based at least one of a mean, a median, or amode of the first response intensity measure and the second responseintensity measure; and calculate the engagement score by one of (1)aggregating the combined response score and the synchrony score, or (2)multiplying the combined response score by the synchrony score.
 17. Thestorage device or storage disc of claim 16, wherein the instructionscause the first processor to assign a first weight to the combinedresponse score and a second weight to the synchrony score.
 18. Thestorage device or storage disc of claim 17, wherein the instructionscause the first processor to assign the first weight and the secondweight based on a content of the first stimulus.
 19. The storage deviceor storage disc of claim 15, wherein the instructions cause the firstprocessor to: event-stamp the first biometric data and the secondbiometric data with respective event indicators indicative of exposureof the first subject and the second subject to an event in the firststimulus; and synchronize the first biometric data and the secondbiometric data based on the event indicators.
 20. The storage device orstorage disc of claim 15, wherein the engagement score includes a firstengagement score for the audience at a first time and a secondengagement score for the audience at a second time and wherein theinstructions cause the first processor to: identify a first engagementpattern based on the first engagement score and the second engagementscore; perform a comparison of the first engagement pattern to a secondengagement pattern, the second engagement pattern associated with athird stimulus; and estimate the effectiveness of the first stimulusbased on the comparison.